Generalized method of moments (GMM) is a generic method for estimating parameters in statistical models. Usually it is applied in the context of semiparametric models, where the parameter of interest is finite-dimensional, whereas the full shape of the distribution function of the data may not be known, and therefore the maximum likelihood estimation is not applicable.
The following matlab project contains the source code and matlab examples used for gmm.
The source code and files included in this project are listed in the project files section, please make sure whether the listed source code meet your needs there.
Mfcc.png in matlab
Community detection use gaussian mixture model in matlab
Fast gmm and fisher vectors in matlab
Ziheng gmm in matlab
Em algorithm for gaussian mixture model with background noise in matlab
Gaussian mixture model in matlab
Useful matlab functions for speaker recognition using adapted gaussian mixture model
Gaussian mixture modeling gui (gmm demo) in matlab
Gmm based expectation maximization algorithm in matlab
Expectation maximization algorithm with gaussian mixture model in matlab
Gaussian mixture model (gmm) gaussian mixture regression (gmr) in matlab
3d visualization of gmm learning via the em algorithm in matlab
Speaker recognition system in matlab
Expectation maximization of gaussian mixture models via cuda in matlab
Wrapper of the jmef java library in matlab
Fast kernel density estimator (multivariate) in matlab
Gmmem based pixel labeling and segmentation in matlab
The lamb toolbox in matlab
Statistical learning toolbox in matlab
Toolkit on econometrics and economics teaching in matlab